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Dissertations / Theses on the topic 'Medical AI'
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1
Baban, Hanna, and Olivia Grauning. "Using Fetal Myocardial Velocity Recordings to Evaluate an AI Platform to Predict High-risk Deliveries." Thesis, KTH, Medicinteknik och hälsosystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255858.
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Diagnosing abnormal fetal cardiac function using ultrasound is a complicated procedure which makes it difficult to obtain high quality results from ultrasound examinations that are performed shortly before delivery. Color tissue Doppler imaging (cTDI) is the echocardiographic technique that has been used to obtain the data for this project. Subtle changes in the fetal cardiac function caused by a variety of complications can possibly be detected using cTDI. Fetuses suffering from these complications are often involved in high-risk deliveries. Combining the data obtained from cTDI with Artificial Intelligence (AI) may improve precision and accuracy when it comes to diagnosing pathological conditions involving fetal cardiac function before delivery. AI uses machines to perform and execute tasks that are characteristic of human intelligence. AI can be achieved by using deep learning. Deep learning uses algorithms called artificial neural networks that are inspired by the biological structure and function of the human brain. The neural networks classify information in a similar manner to the human brain. A platform that uses deep learning can make statements or predictions based on the data fed to it. The AI platform Peltarion uses deep learning to perform tasks. The aim of this project was to use Peltarion to evaluate the possibility of predicting high-risk deliveries with abnormal perinatal outcome by using data obtained by cTDI velocity recordings of the fetal heart. The data included myocardial velocity recordings from 107 pregnancies, out of the 107 pregnancies 82 of the babies were born healthy while 25 babies had an adverse perinatal outcome. The data was uploaded in the platform and three models were built and trained in order to evaluate the performance of the platform using the data. The parameters that have been used to determine the results are loss, accuracy and precision. The results showed that the accuracy parameter was measured to be 0.8 in all cases which means that the model correctly predicts if a fetal heart is healthy or likely to have an adverse outcome 80% of the time. The precision parameter was measured to be around 0.4 which means out of all the times the model predicted a fetal heart to have an adverse outcome, only 40% truly had an adverse outcome. It was concluded that a substantially larger amount of evenly distributed data is required to appropriately evaluate the possibility of using fetal myocardial velocity recordings as data for the AI platform Peltarion to predict high-risk deliveries.
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Hägnestrand, Ida, and Söraas Nina Lindström. "Artificiell intelligens för radiologisk diagnostisering av knäartros : Hur bildkvalitetsförsämringar påverkar en AI-programvaras diagnostisering." Thesis, KTH, Medicinteknik och hälsosystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298228.
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Framgången av mönsterigenkänning inom AI (artificiell intelligens) har skapat höga förväntningar om att AI ska kunna appliceras inom vården, framför allt inom radiologi. Det danska företaget Radiobotics har utvecklat en maskininlärningsbaserad programvara som diagnostiserar knäartros, för att assistera vårdpersonalen i deras arbete. Denna AI-programvara vid namn RBknee analyserar en röntgenbild utifrån tre diagnostiska parametrar som förekommer vid knäartros, för att sedan sammanställa de radiologiska fynden i en skriftlig rapport tillsammans med en slutgiltig diagnos. För att få förståelse för hur RBknees analysförmåga påverkas av en bildkvalitetsförsämring undersöktes för vilken kontrast och brusnivå som RBknee genererar ett felaktigt utlåtande gällande de diagnostiska parametrarna och slutdiagnosen. Vidare undersöktes om graden av knäartros påverkade RBknee analysförmåga vid en bildkvalitetsförsämring. Ett bildunderlag med kliniskt tagna slätröntgenbilder av knän degraderades med avseende på kontrast och brus för att sedan analyseras av RBknee. Förändringar av RBknees utlåtande för de degraderade bilderna jämfört med originalbildens utlåtande sammanställdes och studerades. Resultatet visade att det inte gick att identifiera en specifik försämringsgrad av bildkvaliteten där RBknee genererade ett felaktigt utlåtande. RBknees förmåga att generera ett korrekt utlåtande var bättre vid en kontrastdegradering än vid en brusdegradering. Det konstaterades att en ökad brusnivå ökade risken för ett felaktigt utlåtande av RBknee, samt att brusets position på röntgenbilden hade en påverkan. Det gick även att fastställa att röntgenbilder av knän med en lägre grad av knäartros i högre grad riskerade att få felaktiga utlåtanden av RBknee.The success of pattern recognition in AI (artificial intelligence) has brought high expectations for AI to be applied in healthcare, especially in radiology. A machine learning software for knee osteoarthritis diagnosis has been developed by the Danish company Radiobotics. The AI software, named RBknee, analyses digital radiographs and annotates osteoarthritis related findings. The findings, together with a conclusion, are compiled in a written report. RBknee is intended to assist healthcare professionals in radiographic analysis. How RBknees analytical ability is affected by a reduced image quality was studied by examining the contrast and noise level which cause RBknee to generate incorrect findings and conclusions. If the image quality reduction caused RBknees analytically ability to differ with different degrees of knee osteoarthritis, was also studied. The image quality of clinical digital radiographs of knees was reduced and analysed by RBknee. RBknees findings and conclusion were compared with the report of the original image, where the changes were compiled into tables. No specific reduction of image quality that restricted RBknee analytically ability was established in the study. An increased noise level seemed to increase the risk of receiving an incorrect report by RBknee. RBknees ability to generate correct report was better for contrast degraded images than for images with increased noise level. The position of the noise in the radiograph also seemed to have an impact on RBknees analytical ability. It was also possible to establish that knees with a lower degree of knee osteoarthritis were more likely to receive an incorrect report from RBknee.
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Björklund, Pernilla. "The curious case of artificial intelligence : An analysis of the relationship between the EU medical device regulations and algorithmic decision systems used within the medical domain." Thesis, Uppsala universitet, Juridiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442122.
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The healthcare sector has become a key area for the development and application of new technology and, not least, Artificial Intelligence (AI). New reports are constantly being published about how this algorithm-based technology supports or performs various medical tasks. These illustrates the rapid development of AI that is taking place within healthcare and how algorithms are increasingly involved in systems and medical devices designed to support medical decision-making. The digital revolution and the advancement of AI technologies represent a step change in the way healthcare may be delivered, medical services coordinated and well-being supported. It could allow for easier and faster communication, earlier and more accurate diagnosing and better healthcare at lower costs. However, systems and devices relying on AI differs significantly from other, traditional, medical devices. AI algorithms are – by nature – complex and partly unpredictable. Additionally, varying levels of opacity has made it hard, sometimes impossible, to interpret and explain recommendations or decisions made by or with support from algorithmic decision systems. These characteristics of AI technology raise important technological, practical, ethical and regulatory issues. The objective of this thesis is to analyse the relationship between the EU regulation on medical devices (MDR) and algorithmic decision systems (ADS) used within the medical domain. The principal question is whether the MDR is enough to guarantee safe and robust ADS within the European healthcare sector or if complementary (or completely different) regulation is necessary. In essence, it will be argued that (i) while ADS are heavily reliant on the quality and representativeness of underlying datasets, there are no requirements with regard to the quality or composition of these datasets in the MDR, (ii) while it is believed that ADS will lead to historically unprecedented changes in healthcare , the regulation lacks guidance on how to manage novel risks and hazards, unique to ADS, and that (iii) as increasingly autonomous systems continue to challenge the existing perceptions of how safety and performance is best maintained, new mechanisms (for transparency, human control and accountability) must be incorporated in the systems. It will also be found that the ability of ADS to change after market certification, will eventually necessitate radical changes in the current regulation and a new regulatory paradigm might be needed.
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Söllner, Michaela [Verfasser], Jörg [Akademischer Betreuer] Königstorfer, Jörg [Gutachter] Königstorfer, and Martina [Gutachter] Steul-Fischer. "Paving the Way for Medical AI: Consumer Response to Artificial Intelligence in Healthcare / Michaela Söllner ; Gutachter: Jörg Königstorfer, Martina Steul-Fischer ; Betreuer: Jörg Königstorfer." München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1231434643/34.
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Kantedal, Simon. "Evaluating Segmentation of MR Volumes Using Predictive Models and Machine Learning." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171102.
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A reliable evaluation system is essential for every automatic process. While techniques for automatic segmentation of images have been extensively researched in recent years, evaluation of the same has not received an equal amount of attention. Amra Medical AB has developed a system for automatic segmentation of magnetic resonance (MR) images of human bodies using an atlas-based approach. Through their software, Amra is able to derive body composition measurements, such as muscle and fat volumes, from the segmented MR images. As of now, the automatic segmentations are quality controlled by clinical experts to ensure their correctness. This thesis investigates the possibilities to leverage predictive modelling to reduce the need for a manual quality control (QC) step in an otherwise automatic process. Two different regression approaches have been implemented as a part of this study: body composition measurement prediction (BCMP) and manual correction prediction (MCP). BCMP aims at predicting the derived body composition measurements and comparing the predictions to actual measurements. The theory is that large deviations between the predictions and the measurements signify an erroneously segmented sample. MCP instead tries to directly predict the amount of manual correction needed for each sample. Several regression models have been implemented and evaluated for the two approaches. Comparison of the regression models shows that local linear regression (LLR) is the most performant model for both BCMP and MCP. The results show that the inaccuracies in the BCMP-models, in practice, renders this approach useless. MCP proved to be a far more viable approach; using MCP together with LLR achieves a high true positive rate with a reasonably low false positive rate for several body composition measurements. These results suggest that the type of system developed in this thesis has the potential to reduce the need for manual inspections of the automatic segmentation masks.
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Johnson, Beverly Elaine. "Attitudes and Perceptions of Mental Health Treatment for Native American Clients." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4524.
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The need for mental health service is increasing in American Indian/Alaska Native (AI/AN) communities. While research has examined the availability, access, and effectiveness of provided services to the AI/AN, very little is known about the influence of the attitude and perceptions of both clinicians and clients in their therapeutic relationship in the treatment process. Using the frameworks of liberation, oppression, and trauma theory, this qualitative phenomenological study explored mental health service delivery and utilization issues within an AI/AN community. Data were collected through semistructured interviews with 14 clinician and client participants. The data were sorted into themes and subthemes and analyzed using the NVivo 11 computer software. Intergenerational struggle represented the primary theme and other subthemes such as assimilation, acculturation, and communication were among some of the secondary themes gathered from the data. Analysis of the themes provided greater insights into the dynamics of the participant's lived experience in various organizational structures within the larger community as well as a better understanding of mental health service delivery and utilization in maintaining sobriety in their daily struggles. The results indicated that intergenerational struggle along with other environmental factors were the chief causes of their cyclical journey through the penal and other systems; thus reducing their ability in maintaining longer sobriety and in improving their mental health. The implications for positive social change in this study include the reduction of stigma associated with these health issues through the education of the community and in training clinicians in factor-specific issues impacting life altering critical events in AI/AN struggles.
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Walker, Donald. "Similarity Determination and Case Retrieval in an Intelligent Decision Support System for Diabetes Management." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1194562654.
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Mercatali, Martina. "La traduzione medica ai tempi del Coronavirus: i protocolli clinici per il trattamento della malattia da COVID-19." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
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Il presente elaborato mira a illustrare l'importanza della traduzione medica, presentando e analizzando la traduzione di 10 protocolli clinici sulla sperimentazione di farmaci per il trattamento della malattia COVID-19.
Il primo capitolo sarà una panoramica sulle lingue speciali per presentare le caratteristiche del linguaggio tecnico-scientifico. Successivamente, si indagherà sull'importanza della traduzione nella divulgazione scientifica, concentrandosi in particolare sulla traduzione medica, proponendo un excursus storico e spiegando perché la medicina è stato il primo ambito scientifico a prosperare grazie alla traduzione. Il secondo capitolo sarà dedicato agli studi e ai protocolli clinici sia da un punto di vista storico che attuale, prendendo in considerazione come la pandemia da COVID-19 abbia influenzato la necessità di traduzione nella sperimentazione clinica. Il terzo capitolo delineerà il progetto finale dell’elaborato, concentrandosi sulla base teorica dell'analisi del testo di partenza orientata alla traduzione che sarà poi applicata ai testi in questione. Il quarto e ultimo capitolo presenterà i principali problemi riscontrati durante il processo di traduzione, focalizzandosi su questioni pragmatiche, convenzionali e linguistiche che saranno poi prese in considerazione per giustificare le strategie traduttive adottate.
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Zuffa, Elisa <1979>. "La suscettibilità genetica al linfoma di Hodgkin e ai tumori secondari: due storie o due capitoli della stessa storia?" Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/994/1/Tesi_Zuffa_Elisa.pdf.
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Zuffa, Elisa <1979>. "La suscettibilità genetica al linfoma di Hodgkin e ai tumori secondari: due storie o due capitoli della stessa storia?" Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/994/.
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Stracke, Henning. "Auswirkungen eines Statins auf den In-vivo-Metabolismus von HDL-Apo AI dargestellt mit stabilen Isotopen /." Marburg : Görich und Weiershäuser, 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014591948&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Abessi, Ovais. "Leaflet Material Selection for Aortic Valve Repair." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/30191.
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Leaflet replacement in aortic valve repair (AVr) is associated with increased long-term repair failure. Hemodynamic performance and mechanical stress levels were investigated after porcine AVr with 5 types of clinically relevant replacement materials to ascertain which material(s) would be best suited for repair. Porcine aortic roots with intact aortic valves were placed in a left-heart simulator mounted with a high-speed camera for baseline valve assessment. Then, the non-coronary leaflet was excised and replaced with autologous porcine pericardium (APP), glutaraldehyde-fixed bovine pericardial patch (BPP; Synovis™), extracellular matrix scaffold (CorMatrix™), or collagen-impregnated Dacron (HEMASHIELD™). Hemodynamic parameters were measured over a range of cardiac outputs (2.5–6.5L/min) post-repair. Material properties of the above materials along with St. Jude Medical™ Pericardial Patch with EnCapTM Technology (SJM) were determined using pressurization experiments. Finite element models of the aortic valve and root complex were then constructed to verify the hemodynamic characteristics and determine leaflet stress levels.
This study demonstrates that APP and SJM have the closest profiles to normal aortic valves; therefore, use of either replacement material may be best suited. Increased stresses found in BPP, HEMASHIELD™, and CorMatrix™ groups may be associated with late repair failure.
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Liao, Chih-Kao, and 廖志高. "A Strategic Foresight of AI-Assisted Medical Imaging." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9hnqp6.
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碩士國立交通大學
管理學院科技管理學程
106
This thesis focuses on an analysis of business models for artifical intelligence service in medical imaging. By applying an integrated model of Innovation Intensive Service (IIS), it analyzes the current and future strategic positioning of AI assistant in medical imaging from the aspect of internal value activities and externalities. The internal value activities contains 6 key factors which includes Design, Validation of Testing, Marketing, Delivery, After Service and Supporting Activities, while the Externalities comprise 7 key factors which are Complementary Assets Supplier, R&D, Technology, Production, Servicing, Market and Other Users. Last but not least, structuring a matrix, which encompasses four customization degrees abd five innovation modes, to illustrate the strategic development trend of future in next 5-10 years. The sources of statistics data for IIS are collected through questionnaires and interviews with industry experts. The result of this study indicates that the present positioning of AI assistant in medical imagining via questionnaires and interviews with industry experts is at "Product Innovation (P1)/Generic Service (G)", while the future positioning is at "Structure Innovation (S)/ Selective Service (S)". However, by analysis through IIS, the future strategic positioning mentioned above should be repositioned as "Product Innovation (P1)/Generic Service (G)", since the one suggested by industry experts is not feasible. The companies may follow the study result to strengthen key successul factors accordingly in order to increase industrial value and competitiveness. Keywords: Artifical Intelligence, Medical Imaging, Innovation Intensive Services, IIS, Internal Value Activities, Externalities
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Wang, Ching-Fu, and 王經富. "Wearable Internet of Things-based Medical and Fitness Expert AI-platform." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/n4rfdr.
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博士國立陽明大學
生物醫學工程學系
107
Cardiovascular disease (CVD) is the leading cause of the death all over the world, and this health issues also bring about abundant economic burdens. The global popular technology, Internet of Things (IoT), can integrate the hardware system of health monitoring, diagnostics and treatment, and making it more personalized, timely, and convenient in a lower cost. Wearable IoT may monitor vital signs and physical activities and promote a health program to maintain an active lifestyle, develop healthy habits for reducing the morbidity of CVD. However, the existing wearable devices still confront big challenges of insufficient function and poor strategy of big data acquisition for bio-data analysis. Therefore, this study proposed a wearable Hardware/Software (HW/SW) co-design wrist-type PPG device for IoT healthcare system, which incorporate with 24-hours vital sign AI-monitoring. To verify the clinical requirement, this study conducted a long-term clinical trial to validate different function including heart rate variability, blood pressure trend, atrial fibrillation, blood oxygen level, sleep cycle. Artificial intelligence and machine learning techniques are used to increase the measuring accuracy. The results shown that our lab-developed wrist-type PPG device was verified to acquire the sufficient and reliable bio-data. The AI-platform was also successfully established to provide specialists and users helpful information, such as timely noticing the abnormal vital signs or long-term healthy trend shown on the terminal device interface. By these means, we expecting the quality of healthy life would be raised up.
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Oakden-Rayner, Luke. "Closing the implementation gap in pre-deployment medical AI study design." Thesis, 2021. https://hdl.handle.net/2440/136684.
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The rapid development of clinical artificial intelligence, AI, technologies has outpaced the development of robust regulatory and clinical safety mechanisms. AI systems are cleared for use and deployed in practice relying on pre-clinical performance studies, without evidence of the impact this will have on patient and provider outcomes. This has led to concerns of an, implementation gap, where systems that appear to perform well on pre-clinical testing fail to produce the expected outcomes in practice. While there is an urgent need for direct clinical testing of AI systems and evaluation of the impact of these systems on patient and provider outcomes, it is implausible to expect the clinical evaluation will be performed at the scale necessary to mitigate potential AI harms of the many AI systems already in use and currently under development. In this body of work I look at factors which may contribute to the implementation gap, in particular the effects of low-quality training and testing data, flawed and incomplete study design methodologies, and an over-reliance on explainability methods to address safety. I suggest a series of improvements to how we design, evaluate, and utilise AI systems in clinical practice, with the goal of better estimating the potential harms of AI during the pre-clinical testing phase, and by doing so closing the implementation gap.Thesis (Ph.D.) -- University of Adelaide, School of Public Health, 2022
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Timoner, Samson. "Compact Representations for Fast Nonrigid Registration of Medical Images." 2003. http://hdl.handle.net/1721.1/7110.
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We develop efficient techniques for the non-rigid registration of medical images by using representations that adapt to the anatomy found in such images. Images of anatomical structures typically have uniform intensity interiors and smooth boundaries. We create methods to represent such regions compactly using tetrahedra. Unlike voxel-based representations, tetrahedra can accurately describe the expected smooth surfaces of medical objects. Furthermore, the interior of such objects can be represented using a small number of tetrahedra. Rather than describing a medical object using tens of thousands of voxels, our representations generally contain only a few thousand elements. Tetrahedra facilitate the creation of efficient non-rigid registration algorithms based on finite element methods (FEM). We create a fast, FEM-based method to non-rigidly register segmented anatomical structures from two subjects. Using our compact tetrahedral representations, this method generally requires less than one minute of processing time on a desktop PC. We also create a novel method for the non-rigid registration of gray scale images. To facilitate a fast method, we create a tetrahedral representation of a displacement field that automatically adapts to both the anatomy in an image and to the displacement field. The resulting algorithm has a computational cost that is dominated by the number of nodes in the mesh (about 10,000), rather than the number of voxels in an image (nearly 10,000,000). For many non-rigid registration problems, we can find a transformation from one image to another in five minutes. This speed is important as it allows use of the algorithm during surgery. We apply our algorithms to find correlations between the shape of anatomical structures and the presence of schizophrenia. We show that a study based on our representations outperforms studies based on other representations. We also use the results of our non-rigid registration algorithm as the basis of a segmentation algorithm. That algorithm also outperforms other methods in our tests, producing smoother segmentations and more accurately reproducing manual segmentations.
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Zollei, Lilla. "2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images." 2001. http://hdl.handle.net/1721.1/7078.
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The registration of pre-operative volumetric datasets to intra- operative two-dimensional images provides an improved way of verifying patient position and medical instrument loca- tion. In applications from orthopedics to neurosurgery, it has a great value in maintaining up-to-date information about changes due to intervention. We propose a mutual information- based registration algorithm to establish the proper align- ment. For optimization purposes, we compare the perfor- mance of the non-gradient Powell method and two slightly di erent versions of a stochastic gradient ascent strategy: one using a sparsely sampled histogramming approach and the other Parzen windowing to carry out probability density approximation. Our main contribution lies in adopting the stochastic ap- proximation scheme successfully applied in 3D-3D registra- tion problems to the 2D-3D scenario, which obviates the need for the generation of full DRRs at each iteration of pose op- timization. This facilitates a considerable savings in compu- tation expense. We also introduce a new probability density estimator for image intensities via sparse histogramming, de- rive gradient estimates for the density measures required by the maximization procedure and introduce the framework for a multiresolution strategy to the problem. Registration results are presented on uoroscopy and CT datasets of a plastic pelvis and a real skull, and on a high-resolution CT- derived simulated dataset of a real skull, a plastic skull, a plastic pelvis and a plastic lumbar spine segment.
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CHEN, YU-TING, and 陳鈺婷. "Implementation of AI E-Commerce Model for Medical Beauty Industry： A Case Study in Taiwan." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/8nn88u.
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碩士中國文化大學
企業實務管理數位碩士在職專班
107
The current popular and innovative trend is the medical cosmetology industry. If the medical cosmetology industry, considering Taiwan as an example, uses the e-commerce combined with the virtual reality (VR) mode artificial intelligence (AI), and install technology to apply it to the medical cosmetology industry, considerable economic benefits in the future market is expected. Hence, with the auxiliary assistance of technical application of virtual reality (VR) for e-commerce and artificial intelligence (AI), the medical cosmetics users use big data analysis and facial and other related information to assist them to be more critical and focused in problems solving and decision-making assessments, at the same time, task performance. This study mainly investigates the introduction of artificial intelligence e-commerce model in the medical cosmetology industry, taking Taiwan as the model, and base on the Technology Adaption theory, Theory of Reasoned Action and Transaction Cost Theory and using knowledge sharing to adjust the effect, adopts the research method of questionnaire method, and the structural analysis of each facet is carried out by the structural equation (SEM). The scope of research is based on the medical cosmetology industry and information technology in Taiwan as the example and a random sample questionnaire is conducted for in-service personnel of related fields. The questionnaires were distributed to the related parties from December 2018 till January 2019, and 180 valid questionnaires were collected. The conclusion of the study is to introduce Implementation of AI E-Commerce Model for Medical Beauty Industry: A Case Study in Taiwan, proposes that the main strategic direction and research concept can be applied between industries, creating a new perspective and research contribution for the medical cosmetology industry. The concept of results can also be applied to other related industries.
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Lee, Yi-Hsuan, and 李依瑄. "Dynamic AI-Driven Priority-Based Packet Scheduling for Wireless Medical Networks with Selfish and Unselfish Users." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/44p9nc.
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碩士國立交通大學
電信工程研究所
106
In this thesis, we propose an AI-driven priority-based scheduling algorithm for wireless medical networks with the selfish and unselfish gateways. Unlike most of existing works, we focus on beyond wireless body area network (beyond-WBAN) communications between gateways and the base station. We propose an intelligent priority-based packet scheduling algorithm. For the expectation-based detection scheme, we derive analytic results that are consistent with simulation results. In addition, we proposed a novel AI-based scheme for the BS to detect the selfish misbehavior of the gateway. Simulation results show that the proposed AI-based approach outperforms the expectation-based approach. Furthermore, we use simulation results to show that the proposed priority-based scheme is superior to the non-priority scheme in terms of providing differentiated quality-of-services to users.
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Chen, Tang-Ying, and 陳棠英. "The Business Model Analysis of Emerging AI Technology Industry - Taking the Smart Medical Industry as an Example." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/bhkj3f.
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碩士國立交通大學
管理學院科技管理學程
107
The dramatic improvement of the three core conditions: the rapid growth of IoT and the internet, the modern algorithms, big data, and hardware computing that have led to the widespread use of AI (artificial intelligence). Customers' behavior is changing, and it's necessary for the traditional industry to modify the business model to bring innovation as technology is improving. The purpose of this study was to investigate the business model of smart medical industry - the medical image improvement company in response to artificial intelligence technology to discover how enterprises to face the trend and gain the key elements to create a successful business model. This study used Gary Hamel's business model as the framework that includes four aspects: Core Strategy, Strategic Resources, Customer Interface, Value Network, and three communicating bridges (Customer Benefits, Configuration, and Company Boundaries). In order to find out the most suitable business model for the medical image improvement as a reference for enterprises to enhance their advantages, I did expert interviews to verify the feasibility and key elements of the business model.
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Pohl, Kilian M., John Fisher, W. Eric L. Grimson, and William M. Wells. "An Expectation Maximization Approach for Integrated Registration, Segmentation, and Intensity Correction." 2005. http://hdl.handle.net/1721.1/30532.
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This paper presents a statistical framework which combines the registration of an atlas with the segmentation of MR images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given for a brain structure-dependent affine mapping approach. The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 30 brain MR images. In addition, we show that the approach performs better than similar methods which separate the registration from the segmentation problem.
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Hardy, Maryann L., and H. Harvey. "Artificial intelligence in diagnostic imaging: impact on the radiography profession." 2019. http://hdl.handle.net/10454/17732.
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YesThe arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.
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"Designing an AI-driven System at Scale for Detection of Abusive Head Trauma using Domain Modeling." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.57221.
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abstract: Traumatic injuries are the leading cause of death in children under 18, with head trauma being the leading cause of death in children below 5. A large but unknown number of traumatic injuries are non-accidental, i.e. inflicted. The lack of sensitivity and specificity required to diagnose Abusive Head Trauma (AHT) from radiological studies results in putting the children at risk of re-injury and death. Modern Deep Learning techniques can be utilized to detect Abusive Head Trauma using Computer Tomography (CT) scans. Training models using these techniques are only a part of building AI-driven Computer-Aided Diagnostic systems. There are challenges in deploying the models to make them highly available and scalable.
The thesis models the domain of Abusive Head Trauma using Deep Learning techniques and builds an AI-driven System at scale using best Software Engineering Practices. It has been done in collaboration with Phoenix Children Hospital (PCH). The thesis breaks down AHT into sub-domains of Medical Knowledge, Data Collection, Data Pre-processing, Image Generation, Image Classification, Building APIs, Containers and Kubernetes. Data Collection and Pre-processing were done at PCH with the help of trauma researchers and radiologists. Experiments are run using Deep Learning models such as DCGAN (for Image Generation), Pretrained 2D and custom 3D CNN classifiers for the classification tasks. The trained models are exposed as APIs using the Flask web framework, contained using Docker and deployed on a Kubernetes cluster.
The results are analyzed based on the accuracy of the models, the feasibility of their implementation as APIs and load testing the Kubernetes cluster. They suggest the need for Data Annotation at the Slice level for CT scans and an increase in the Data Collection process. Load Testing reveals the auto-scalability feature of the cluster to serve a high number of requests.Dissertation/Thesis
Masters Thesis Software Engineering 2020
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(7242737), Pradeep Periasamy. "Generative Adversarial Networks for Lupus Diagnostics." Thesis, 2019.
Find full textAbstract:
The recent boom of Machine Learning Network Architectures like Generative
Adversarial Networks (GAN), Deep Convolution Generative Adversarial Networks
(DCGAN), Self Attention Generative Adversarial Networks (SAGAN), Context
Conditional Generative Adversarial Networks (CCGAN) and the development of
high-performance computing for big data analysis has the potential to be highly
beneficial in many domains and fittingly in the early detection of chronic diseases.
The clinical heterogeneity of one such chronic auto-immune disease like Systemic
Lupus Erythematosus (SLE), also known as Lupus, makes it difficult for medical
diagnostics. One major concern is a limited dataset that is available for diagnostics.
In this research, we demonstrate the application of Generative Adversarial Networks
for data augmentation and improving the error rates of Convolution Neural
Networks (CNN). Limited Lupus dataset of 30 typical ’butterfly rash’ images is used
as a model to decrease the error rates of a widely accepted CNN architecture like
Le-Net. For the Lupus dataset, it can be seen that there is a 73.22% decrease in the
error rates of Le-Net. Therefore such an approach can be extended to most recent
Neural Network classifiers like ResNet. Additionally, a human perceptual study
reveals that the artificial images generated from CCGAN are preferred to closely
resemble real Lupus images over the artificial images generated from SAGAN and
DCGAN by 45 Amazon MTurk participants. These participants are identified as
’healthcare professionals’ in the Amazon MTurk platform. This research aims to
help reduce the time in detection and treatment of Lupus which usually takes 6 to 9
months from its onset.
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Striani, Manuel. "A Knowledge-based abstraction framework for trace comparison and semantic process mining." Doctoral thesis, 2019. http://hdl.handle.net/2318/1712735.
Full textAbstract:
Event logs constitute a rich source of information for several process analysis activities, which can take advantage of similar traces retrieval.
The capability of relating semantic structures such as taxonomies to actions in the traces can enable trace comparison to work at different levels of abstraction and, therefore, to mask irrelevant details, and make the identfication of similar traces much more flexible. For this reason, this thesis proposes a trace abstraction mechanism based on domain knowledge, which maps actions in the log traces to instances of ground concepts in an ontology, and then allows one to generalize them up to the desired level. Abstracted traces are also provided
as an input to semantic process mining; finally, abstracted models (i.e., models mined from abstracted traces) can be compared
and ranked, by adopting a similarity metric, able to take into account penalties collected during the abstraction phase.
The overall framework has been tested in the eld of stroke management, where we were able to cluster similar traces, corresponding to
correct medical behaviors, abstracting from details, but still preserving the capabilities of identifying outlying situations. Moreover, we could mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible. Finally, we were able to rank abstract process models more similarly to the ordering provided by a domain expert, with respect to what could be obtained when working on non-abstract ones.